Our Data, Ourselves: Privacy Via Distributed Noise Generation

  title={Our Data, Ourselves: Privacy Via Distributed Noise Generation},
  author={Cynthia Dwork and Krishnaram Kenthapadi and Frank McSherry and Ilya Mironov and Moni Naor},
In this work we provide efficient distributed protocols for generating shares of random noise, secure against malicious participants. The purpose of the noise generation is to create a distributed implementation of the privacy-preserving statistical databases described in recent papers [14, 4, 13]. In these databases, privacy is obtained by perturbing the true answer to a database query by the addition of a small amount of Gaussian or exponentially distributed random noise. The computational… CONTINUE READING
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